docs: def predict_factors(self, counts_df, maxiter=10, ncores=1, random_seed=1, stop_thr=1e-3, return_all=False):
  		"""
  		Gets latent factors for a user given her item counts
  
  		This is similar to obtaining topics for a document in LDA.
  
  		Note
  		----
  		This function will NOT modify any of the item parameters.
  
  		Note
  		----
  		This function only works with one user at a time.
  
  		Note
  		----
  		This function is prone to producing all NaNs values.
  
  		Parameters
  		----------
  		counts_df : DataFrame or array (nsamples, 2)
  			Data Frame with columns 'ItemId' and 'Count', indicating the non-zero item counts for a
  			user for whom it's desired to obtain latent factors.
  		maxiter : int
  			Maximum number of iterations to run.
  		ncores : int
  			Number of threads/cores to use. With data for only one user, it's unlikely that using
  			multiple threads would give a significant speed-up, and it might even end up making
  			the function slower due to the overhead.
  			If passing -1, it will determine the maximum number of cores in the system and use that.
  		random_seed : int
  			Random seed used to initialize parameters.
  		stop_thr : float
  			If the l2-norm of the difference between values of Theta_{u} between interations is less
  			than this, it will stop. Smaller values of 'k' should require smaller thresholds.
  		return_all : bool
  			Whether to return also the intermediate calculations (Gamma_shp, Gamma_rte). When
  			passing True here, the output will be a tuple containing (Theta, Gamma_shp, Gamma_rte, Phi)
  
  		Returns
  		-------
  		latent_factors : array (k,)
  			Calculated latent factors for the user, given the input data
  		"""
  
  		ncores, random_seed, stop_thr, maxiter = self._check_input_predict_factors(ncores, random_seed, stop_thr, maxiter)
  
  		## processing the data
  		counts_df = self._process_data_single(counts_df)
  
  		## calculating the latent factors
  		Theta = np.empty(self.k, dtype='float32')
  		temp = cython_loops.calc_user_factors(
  								 self.a, self.a_prime, self.b_prime,
  								 self.c, self.c_prime, self.d_prime,
  								 counts_df.Count.values,
  								 counts_df.ItemId.values,
  								 Theta, self.Beta,
  								 self.Lambda_shp,
  								 self.Lambda_rte,
  								 cython_loops.cast_ind_type(counts_df.shape[0]), cython_loops.cast_ind_type(self.k),
  								 cython_loops.cast_int(int(maxiter)), cython_loops.cast_int(ncores),
  								 cython_loops.cast_int(int(random_seed)), cython_loops.cast_float(stop_thr),
  								 cython_loops.cast_int(bool(return_all))
  								 )
  
  		if np.isnan(Theta).sum() > 0:
  			raise ValueError("NaNs encountered in the result. Failed to produce latent factors.")
  
  		if return_all:
  			return (Theta, temp[0], temp[1], temp[2])
  		else:
  			return Theta